Data Science Dojo

SEPTEMBER 10, 2024

The company’s Lakehouse Platform, which merges data warehousing and data lakes, empowers data scientists and ML engineers to process, store, analyze, and even monetize datasets efficiently. This enables the deployment of LLMs at scale, providing the necessary tools and resources for advanced ML and data analytics.

Machine Learning 369

Machine Learning Machine Learning Natural Language Processing ML 369

Tableau

MARCH 29, 2021

we’ve added new connectors to help our customers access more data in Azure than ever before: an Azure SQL Database connector and an Azure Data Lake Storage Gen2 connector. As our customers increasingly adopt the cloud, we continue to make investments that ensure they can access their data anywhere. March 30, 2021.

Azure 52

Azure Tableau Data Lakes SQL 52

Flipboard

AUGUST 17, 2023

Amazon Redshift uses SQL to analyze structured and semi-structured data across data warehouses, operational databases, and data lakes, using AWS-designed hardware and ML to deliver the best price-performance at any scale. If you want to do the process in a low-code/no-code way, you can follow option C.

ML 123

ML ML AWS Data Warehouse 123

AWS Machine Learning Blog

SEPTEMBER 1, 2023

These teams are as follows: Advanced analytics team (data lake and data mesh) – Data engineers are responsible for preparing and ingesting data from multiple sources, building ETL (extract, transform, and load) pipelines to curate and catalog the data, and prepare the necessary historical data for the ML use cases.

AI 128

AI AI ML ML 128

Tableau

SEPTEMBER 22, 2022

It’s a new day for business because we have data to help us understand what customers need, make smarter decisions, and take action fast. Data helps us innovate not only technology, but also customer experiences. And companies need real-time data and analytics, a single source of truth, to meet changing customer expectations. .

Tableau 98

Tableau Analytics Analytics AI 98

Tableau

SEPTEMBER 23, 2022

It’s a new day for business because we have data to help us understand what customers need, make smarter decisions, and take action fast. Data helps us innovate not only technology, but also customer experiences. And companies need real-time data and analytics, a single source of truth, to meet changing customer expectations. .

Tableau 98

Tableau Analytics Analytics AI 98

Alation

NOVEMBER 18, 2021

External Tables Create a Shared View of the Data Lake. We’ve seen external tables become popular with our customers, who use them to provide a normalized relational schema on top of their data lake. Essentially, external tables create a shared view of the data lake, a single pane of glass everyone can reference.

Data Lakes 52

Data Lakes Data Governance SQL AWS 52

Alation

FEBRUARY 13, 2020

This brief definition makes several points about data catalogs—data management, searching, data inventory, and data evaluation—but all depend on the central capability to provide a collection of metadata. Data catalogs have become the standard for metadata management in the age of big data and self-service analytics.

Data Lakes 64

Data Lakes Data Analysis Data Analysis Big Data 64

AWS Machine Learning Blog

AUGUST 15, 2024

In the following sections, we demonstrate how to import and prepare the data, optionally export the data, create a model, and run inference, all in SageMaker Canvas. Download the dataset from Kaggle and upload it to an Amazon Simple Storage Service (Amazon S3) bucket. Explore the future of no-code ML with SageMaker Canvas today.

ML 126

ML ML Data Preparation AWS 126

Alation

FEBRUARY 13, 2020

Data curation is a term that has recently become a common part of data management vocabulary. Data curation is important in today’s world of data sharing and self-service analytics, but I think it is a frequently misused term. Curating data involves much more than storing data in a shared database.

Data Warehouse 52

Data Warehouse Data Lakes Data Governance Analytics 52

Flipboard

JUNE 26, 2023

Companies are faced with the daunting task of ingesting all this data, cleansing it, and using it to provide outstanding customer experience. Typically, companies ingest data from multiple sources into their data lake to derive valuable insights from the data.

AWS 123

AWS ML ML ETL 123

Alation

FEBRUARY 13, 2020

At some level, every enterprise is struggling to connect data to decision-making. In The Forrester Wave: Machine Learning Data Catalogs, 36% to 38% of global data and analytics decision makers reported that their structured, semi-structured, and unstructured data each totaled 1,000 TB or more in 2017, up from only 10% to 14% in 2016.

Data Lakes 52

Data Lakes Data Analyst Analytics Analytics 52

Precisely

JANUARY 25, 2024

Today’s cloud systems excel at high-volume data storage, powerful analytics, AI, and software & systems development. Cloud-based DevOps provides a modern, agile environment for developing and maintaining applications and services that interact with the organization’s mainframe data. Download Best Practice 1.

Data Governance 69

Data Governance Database Cloud Data Data Lakes 69

AWS Machine Learning Blog

FEBRUARY 28, 2024

The solution in this post aims to bring enterprise analytics operations to the next level by shortening the path to your data using natural language. Second, you might need to build text-to-SQL features for every database because data is often not stored in a single target. We use Anthropic Claude v2.1 format('parquet').option('path',

SQL 140

SQL AWS Database ML 140

The MLOps Blog

JANUARY 23, 2023

These tools may have their own versioning system, which can be difficult to integrate with a broader data version control system. For instance, our data lake could contain a variety of relational and non-relational databases, files in different formats, and data stored using different cloud providers. DVC Git LFS neptune.ai

ML 52

ML ML Data Lakes Machine Learning 52

Tableau

SEPTEMBER 22, 2022

It’s a new day for business because we have data to help us understand what customers need, make smarter decisions, and take action fast. Data helps us innovate not only technology, but also customer experiences. And companies need real-time data and analytics, a single source of truth, to meet changing customer expectations. .

Tableau 52

Tableau Analytics Analytics AI 52

DagsHub

OCTOBER 23, 2024

To combine the collected data, you can integrate different data producers into a data lake as a repository. A central repository for unstructured data is beneficial for tasks like analytics and data virtualization. Data Cleaning The next step is to clean the data after ingesting it into the data lake.

Machine Learning 52

Machine Learning Machine Learning Data Lakes AI 52

phData

JULY 10, 2023

Why External Tables are Important Data Ingestion: External tables allow you to easily load data into Snowflake from various external data sources without the need to first stage the data within Snowflake. Data Integration: Snowflake supports seamless integration with other data processing systems and data lakes.

Data Lakes 40

Data Lakes Azure Database AWS 40

The MLOps Blog

JUNE 27, 2023

Databricks Databricks is a cloud-native platform for big data processing, machine learning, and analytics built using the Data Lakehouse architecture. LakeFS LakeFS is an open-source platform that provides data lake versioning and management capabilities.

Machine Learning 134

Machine Learning Machine Learning ML ML 134

phData

DECEMBER 4, 2023

However, if there’s one thing we’ve learned from years of successful cloud data implementations here at phData, it’s the importance of: Defining and implementing processes Building automation, and Performing configuration …even before you create the first user account. Download a free PDF by filling out the form.

Clustering 98

Clustering Database SQL Data Pipeline 98

AWS Machine Learning Blog

AUGUST 4, 2023

Let’s look at the file without downloading it. Data Architect, Data Lake & AI/ML, serving strategic customers. He works closely with customers to understand how AWS can help them solve problems, especially in the AI/ML and analytics space. Copy and paste the link into a new browser tab URL.

ML 97

ML ML AWS AI 97

AWS Machine Learning Blog

AUGUST 2, 2023

Marketing firms store vast amounts of digital data that needs to be centralized, easily searchable, and scalable enabled by data catalogs. A centralized data lake with informative data catalogs would reduce duplication efforts and enable wider sharing of creative content and consistency between teams.

AWS 94

AWS AI AI Machine Learning 94

The MLOps Blog

MAY 17, 2023

A data pipeline is created with the focus of transferring data from a variety of sources into a data warehouse. Further processes or workflows can then easily utilize this data to create business intelligence and analytics solutions. This involves looking at the data structure, relationships, and content.

ETL 59

ETL Data Pipeline ML ML 59

IBM Journey to AI blog

JULY 7, 2023

One such breach occurred in May 2022, when a departing Yahoo employee allegedly downloaded about 570,000 pages of Yahoo’s intellectual property (IP) just minutes after receiving a job offer from one of Yahoo’s competitors. In 2022, it took an average of 277 days to identify and contain a data breach.

Data Governance 40

Data Governance Data Lakes Database Cloud Computing 40

The MLOps Blog

MARCH 15, 2023

Data Processing : You need to save the processed data through computations such as aggregation, filtering and sorting. Data Storage : To store this processed data to retrieve it over time – be it a data warehouse or a data lake.

Data Pipeline 59

Data Pipeline ETL Data Quality SQL 59

The MLOps Blog

JUNE 5, 2023

To explain that a little further, when you think about what those models are, the way that GPT-3 or the other similar language models are trained is on this corpus of data called the Common Crawl, which is essentially the whole internet, right? So data is really important for those still. So what are people doing? Do they click?

ML 52

ML ML Machine Learning Machine Learning 52

Flipboard

DECEMBER 4, 2024

The use of separate data warehouses and lakes has created data silos, leading to problems such as lack of interoperability, duplicate governance efforts, complex architectures, and slower time to value. You can use Amazon SageMaker Lakehouse to achieve unified access to data in both data warehouses and data lakes.

Data Lakes 139

Data Lakes Data Warehouse AWS Database 139

AWS Machine Learning Blog

JANUARY 21, 2025

Download the CloudFormation template and upload it in the Specify template Choose Next. Download knowledge articles You need access to ServiceNow knowledge articles. Download a sample article. Upload the previously downloaded knowledge articles to this S3 bucket. Next you need to sync the data source.

AWS 86

AWS AI AI Data Lakes 86

AWS Machine Learning Blog

OCTOBER 11, 2024

Download the notebook file to use in this post. data # Assing local directory path to a python variable local_data_path = "./data/" data/" # Assign S3 bucket name to a python variable. . About the Authors Vishwa Gupta is a Senior Data Architect with AWS Professional Services. Data Architect, Data Lake at AWS.

Database 106

Database AWS Clustering Data Lakes 106